The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expression

• URL: http://arxiv.org/abs/2207.03572v1
• Date: Thu, 7 Jul 2022 21:09:35 GMT
• Title: The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expression
• Authors: Alice Baird, Panagiotis Tzirakis, Jeffrey A. Brooks, Christopher B. Gregory, Bj\"orn Schuller, Anton Batliner, Dacher Keltner, Alan Cowen
• Abstract summary: This paper describes the four tracks and baseline systems, which use state-of-the-art machine learning methods. This year's competition comprises four tracks using a dataset of 59,299 vocalizations from 1,702 speakers. The baseline performance for each track is obtained by utilizing an end-to-end deep learning model.
• Score: 16.364737403587235
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The ACII Affective Vocal Bursts Workshop & Competition is focused on understanding multiple affective dimensions of vocal bursts: laughs, gasps, cries, screams, and many other non-linguistic vocalizations central to the expression of emotion and to human communication more generally. This year's competition comprises four tracks using a large-scale and in-the-wild dataset of 59,299 vocalizations from 1,702 speakers. The first, the A-VB-High task, requires competition participants to perform a multi-label regression on a novel model for emotion, utilizing ten classes of richly annotated emotional expression intensities, including; Awe, Fear, and Surprise. The second, the A-VB-Two task, utilizes the more conventional 2-dimensional model for emotion, arousal, and valence. The third, the A-VB-Culture task, requires participants to explore the cultural aspects of the dataset, training native-country dependent models. Finally, for the fourth task, A-VB-Type, participants should recognize the type of vocal burst (e.g., laughter, cry, grunt) as an 8-class classification. This paper describes the four tracks and baseline systems, which use state-of-the-art machine learning methods. The baseline performance for each track is obtained by utilizing an end-to-end deep learning model and is as follows: for A-VB-High, a mean (over the 10-dimensions) Concordance Correlation Coefficient (CCC) of 0.5687 CCC is obtained; for A-VB-Two, a mean (over the 2-dimensions) CCC of 0.5084 is obtained; for A-VB-Culture, a mean CCC from the four cultures of 0.4401 is obtained; and for A-VB-Type, the baseline Unweighted Average Recall (UAR) from the 8-classes is 0.4172 UAR.

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